Recognition of Leukemia Sub-types Using Transfer Learning and Extraction of Distinguishable Features Using an Effective Machine Learning Approach

Abstract

Although extensive research has been conducted on leukemia, the disease still accounts for more than 350,000 fatalities annually. Automated Leukemia diagnosis may alter the situation because actions can be taken immediately; as a result, accurate detection of Leukemia has been a subject of interest for researchers. As statistics grow and expand, the need for precise leukemia identification continues to increase. In this study, we investigated a dataset of leukemia that used the WHO classification scheme. We developed a modified DenseNet201 design that achieved an overall accuracy of 99.69% without relying on data augmentation. Additionally, we identified and validated key features for leukemia classification by utilizing three feature extraction approaches (i.e., hu moments, haralick texture and parameter-free threshold adjacency statistics) and several machine learning classifiers (i.e., Gaussian Process, Support Vector Machine, K-Nearest Neighbor or KNN, Extra Trees Classifier, and Logistic regression) that outperformed earlier feature extraction-based techniques.